Applying Social Network Analysis (SNA) to P2P File Sharing Andreas - - PowerPoint PPT Presentation

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Applying Social Network Analysis (SNA) to P2P File Sharing Andreas - - PowerPoint PPT Presentation

Feb 24, 2023 201 likes •952 views

Applying Social Network Analysis (SNA) to P2P File Sharing Andreas Schaufelbhl Robin Stohler Benjamin Brgisser P2P Centralized - Napster Decentralized - Gnutella 0.4 /Freenet Hybrid - Gnutella 0.6 BitTorrent BitTorrent BitTorrent

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Applying Social Network Analysis (SNA) to P2P File Sharing

Andreas Schaufelbühl Robin Stohler Benjamin Bürgisser

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P2P

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Centralized - Napster

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Decentralized - Gnutella 0.4 /Freenet

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Hybrid - Gnutella 0.6

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BitTorrent

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BitTorrent

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BitTorrent

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BitTorrent

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BitTorrent

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SNA

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SNA

Graph with Nodes and Edges Nodes: Individuals or Groups Edges: Relationships, interaction

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Measurements

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Network-centric measurements

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Network size

Counting number of edges/nodes + simple

  • Not significant, just

describing the dimension Networksize(nodes): 7 Networksize(edges): 8

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Network compactness

Edges/possibly existing edges + describes comparative compactness +ratio possible to compare

  • Only a general view, no

statement about specific node/ area

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Average degree

+ describes comparative compactness/cohesion +Node interconnectivity compare to average node centric!

sum of all degres number of nodes

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Diameter

Longest shortest path in network = max {(, )} S(i,j): shortest path between any two nodes i,j +measurement of distance in network

  • scales with the number
  • f nodes no

comparision possible for different sized networks

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Measures of Connectivity

How many edges or nodes to remove until it falls in multiple parts? Searching weakest Link Describes cohesion/reliability high number high reliability

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Global Clustering coefficient

Describes ratio between triangles and triplets Range: [0,1] High global clustering good connectivity

/5 2 = 0.4

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Node-centric measurements

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Degree

Node 3: Degree of 4

Number of edges connected to node +simple +number of connectivities comparable

  • No information about importance of the connectivities
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Betweenness centrality

Sum of all shortest paths connecting two nodes, passing the measured node, divided by all shortest path connecting the same two nodes, including the shortest paths not passing the measured node Node 2: Node 3:

  • 1

2 2 2

  • + 1

2 + 2 2 High number important node

  • Scales with number of nodes

No comparision between different networks Divide by number of nodes + importance of one node

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Closeness centrality

Inverse of farness from one node to all other Showing centrality of a specific node

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Eccentricity

Number of longest shortest path for a node = max ! ", # ∶ # ∈ & e(u): eccentricity d(y,x): shortest path yx

  • scales with number of nodes

How far from the furtest other?

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Eigenvector centrality

  • Assings relative score to a node
  • High scoring neighbours raise the score of the

node

  • Measurement of influence

Examples: Google PageRank, Katz centrality

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Local Clustering coefficent

('(−1) 2 '( Edges in neighborhood possibly existing edges in neighborhood )( = 2|

+,: .+,/0 ∈ 1(, +, ∈ 2 |

'(('( − 1) Num of max edges in N: +Comparative number describing clustering of node

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Coreness, k-core

Largest subgraph of connected nodes, where each node has degree of at least k 1-core Subgraph 2-core Subgraph 3-core Subgraph 4-core Subgraph Rank of node: combination of degree and centrality

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Comparison of Centrality Measurements

Low Degree Low Closeness Low Betweenness High Degree Key player tied to important important/active alters Ego's connections are redundant - communication bypasses him/her High Closeness Key player tied to important important/active alters Probably multiple paths in the network, ego is near many people, but so are many others High Betweenness Ego's few ties are crucial for network flow Very rare cell. Would mean that ego monopolizes the ties from a small number

  • f people to many
  • thers.
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Applications of SNA

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SNA

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Facebook

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Social Network Analysis of Terrorist Networks

Two initial suspects linked to al-Qaeda

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Social Network Analysis of Terrorist Networks

Direct links to original suspects

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Social Network Analysis of Terrorist Networks

Indirect links to original suspects

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Social Network Analysis of Terrorist Networks

Mohammed Atta discovered to be local leader

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Page Rank

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SNA in the Enterprise

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Different possibilities to model a graph

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  • Time
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  • Weight

http://irishbrentgoose.blogspot.ch/2012/07/social-networks-revisited.html

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  • Directed
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One Mode Two Mode

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Our Model of the BitTorrent Network

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Our model of the BitTorrent Network

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Our model

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Our model

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Our model

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Random graph according to our model

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Random graph

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Our model

  • Directed
  • One mode
  • Edge = unspecified number of chunks of a known

file

  • Nodes = Peers

Possible enhancements

  • weighted
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Some interpretations of the measurements

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Interpretation of the measurements

  • Degree Centrality
  • Closeness Centrality
  • Betweenness Centrality
  • Eigenvector Cetrality
  • Clustering Coefficient
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Interpretation of the measurements

  • Degree Centrality
  • Closeness Centrality
  • Betweenness Centrality
  • Eigenvector Cetrality
  • Clustering Coefficient
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Interpretation of the measurements

  • Degree Centrality
  • Closeness Centrality
  • Betweenness Centrality
  • Eigenvector Cetrality
  • Clustering Coefficient
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Interpretation of the measurements

  • Degree Centrality
  • Closeness Centrality
  • Betweenness Centrality
  • Eigenvector Cetrality
  • Clustering Coefficient
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Interpretation of the measurements

  • Degree Centrality
  • Closeness Centrality
  • Betweenness Centrality
  • Eigenvector Cetrality
  • Clustering Coefficient
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Optimization

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Optimization

  • Performance
  • Tracker Localized Algorithm
  • Piecepicker Localized Algorithm
  • Friend list approach
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Optimization

  • System Integrity
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Optimization

  • Free riding
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Conclusion

  • BitTorrent is not the best P2P system to apply SNA

because of the role of the tracker

  • Random Nodes are returned
  • BitTorrent is already a better system then the Beginnings
  • f P2P file sharing systems like Gnutella
  • SNA is a very powerful instrument to get insights of

structures that are hard to see

  • Many measurements depending on the graph

model

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Questions?

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Discussion

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Which (if any) P2P systems do you use and why? Did you experience problems such as free-riding?

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What do you think about free riding in BitTorrent? Is it ok to only consume and not contribute?

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Do you see weaknesses in our model how we modeled the graph of the file distribution systems in BitTorrent? What would you change?

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As we heard from Benjamin SNA’s might be used to enhance the social network in enterprises e.g. By adding new edges Do you see problems with that?

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Do you think that the application of SNA adds or diminishes value of the private usage in facebook?

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What is your opinion about SNA/Information gathering in Facebook, Google+ etc? How far is it allowed to go?

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Friends count is basically a degree measure in facebook, do you see also a use of a closeness or betweenness centrality, why ? Why not?

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Since SNA is a network of relations Could you think

  • f other applications for SNA?

Not in the field of social life?